"use strict";
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/**
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* @license
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* Copyright 2018 Google Inc. All Rights Reserved.
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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* =============================================================================
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*/
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Object.defineProperty(exports, "__esModule", { value: true });
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var engine_1 = require("./engine");
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var tensor_1 = require("./tensor");
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var tensor_util_env_1 = require("./tensor_util_env");
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var util = require("./util");
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/**
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* Provided `f(x)`, returns another function `g(x, dy?)`, which gives the
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* gradient of `f(x)` with respect to `x`.
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*
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* If `dy` is provided, the gradient of `f(x).mul(dy).sum()` with respect to
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* `x` is computed instead. `f(x)` must take a single tensor `x` and return a
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* single tensor `y`. If `f()` takes multiple inputs, use `tf.grads` instead.
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*
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* ```js
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* // f(x) = x ^ 2
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* const f = x => x.square();
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* // f'(x) = 2x
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* const g = tf.grad(f);
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*
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* const x = tf.tensor1d([2, 3]);
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* g(x).print();
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* ```
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*
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* ```js
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* // f(x) = x ^ 3
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* const f = x => x.pow(tf.scalar(3, 'int32'));
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* // f'(x) = 3x ^ 2
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* const g = tf.grad(f);
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* // f''(x) = 6x
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* const gg = tf.grad(g);
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*
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* const x = tf.tensor1d([2, 3]);
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* gg(x).print();
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* ```
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*
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* @param f The function f(x), to compute gradient for.
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*/
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/** @doc {heading: 'Training', subheading: 'Gradients'} */
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function grad(f) {
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util.assert(util.isFunction(f), function () { return 'The f passed in grad(f) must be a function'; });
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return function (x, dy) {
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// x can be of any dtype, thus null as the last argument.
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var $x = tensor_util_env_1.convertToTensor(x, 'x', 'tf.grad', null);
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var $dy = (dy != null) ? tensor_util_env_1.convertToTensor(dy, 'dy', 'tf.grad') : null;
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return engine_1.ENGINE.tidy(function () {
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var _a = engine_1.ENGINE.gradients(function () { return f($x); }, [$x], $dy), value = _a.value, grads = _a.grads;
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if ($dy != null) {
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util.assertShapesMatch(value.shape, $dy.shape, 'The shape of dy passed in grad(f)(x, dy) must match the shape ' +
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'returned by f(x)');
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}
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checkGrads(grads);
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return grads[0];
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});
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};
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}
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exports.grad = grad;
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/**
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* Provided `f(x1, x2,...)`, returns another function `g([x1, x2,...], dy?)`,
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* which gives an array of gradients of `f()` with respect to each input
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* [`x1`,`x2`,...].
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*
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* If `dy` is passed when calling `g()`, the gradient of
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* `f(x1,...).mul(dy).sum()` with respect to each input is computed instead.
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* The provided `f` must take one or more tensors and return a single tensor
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* `y`. If `f()` takes a single input, we recommend using `tf.grad` instead.
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*
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* ```js
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* // f(a, b) = a * b
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* const f = (a, b) => a.mul(b);
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* // df / da = b, df / db = a
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* const g = tf.grads(f);
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*
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* const a = tf.tensor1d([2, 3]);
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* const b = tf.tensor1d([-2, -3]);
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* const [da, db] = g([a, b]);
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* console.log('da');
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* da.print();
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* console.log('db');
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* db.print();
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* ```
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*
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* @param f The function `f(x1, x2,...)` to compute gradients for.
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*/
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/** @doc {heading: 'Training', subheading: 'Gradients'} */
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function grads(f) {
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util.assert(util.isFunction(f), function () { return 'The f passed in grads(f) must be a function'; });
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return function (args, dy) {
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util.assert(Array.isArray(args), function () { return 'The args passed in grads(f)(args) must be an array ' +
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'of `Tensor`s or `TensorLike`s'; });
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// args can be of any dtype, thus null as the last argument.
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var $args = tensor_util_env_1.convertToTensorArray(args, 'args', 'tf.grads', null);
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var $dy = (dy != null) ? tensor_util_env_1.convertToTensor(dy, 'dy', 'tf.grads') : null;
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return engine_1.ENGINE.tidy(function () {
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var _a = engine_1.ENGINE.gradients(function () { return f.apply(void 0, $args); }, $args, $dy), value = _a.value, grads = _a.grads;
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if ($dy != null) {
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util.assertShapesMatch(value.shape, $dy.shape, 'The shape of dy passed in grads(f)([x1,...], dy) must ' +
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'match the shape returned by f([x1,...])');
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}
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checkGrads(grads);
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return grads;
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});
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};
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}
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exports.grads = grads;
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/**
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* Like `tf.grad`, but also returns the value of `f()`. Useful when `f()`
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* returns a metric you want to show.
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*
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* The result is a rich object with the following properties:
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* - grad: The gradient of `f(x)` w.r.t `x` (result of `tf.grad`).
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* - value: The value returned by `f(x)`.
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*
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* ```js
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* // f(x) = x ^ 2
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* const f = x => x.square();
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* // f'(x) = 2x
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* const g = tf.valueAndGrad(f);
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*
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* const x = tf.tensor1d([2, 3]);
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* const {value, grad} = g(x);
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*
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* console.log('value');
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* value.print();
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* console.log('grad');
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* grad.print();
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* ```
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*/
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/** @doc {heading: 'Training', subheading: 'Gradients'} */
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function valueAndGrad(f) {
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util.assert(util.isFunction(f), function () { return 'The f passed in valueAndGrad(f) must be a function'; });
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return function (x, dy) {
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util.assert(x instanceof tensor_1.Tensor, function () { return 'The x passed in valueAndGrad(f)(x) must be a tensor'; });
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util.assert(dy == null || dy instanceof tensor_1.Tensor, function () { return 'The dy passed in valueAndGrad(f)(x, dy) must be a tensor'; });
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var _a = engine_1.ENGINE.gradients(function () { return f(x); }, [x], dy), grads = _a.grads, value = _a.value;
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checkGrads(grads);
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return { grad: grads[0], value: value };
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};
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}
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exports.valueAndGrad = valueAndGrad;
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/**
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* Like `tf.grads`, but returns also the value of `f()`. Useful when `f()`
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* returns a metric you want to show.
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*
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* The result is a rich object with the following properties:
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* - grads: The gradients of `f()` w.r.t each input (result of `tf.grads`).
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* - value: The value returned by `f(x)`.
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*
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* ```js
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* // f(a, b) = a * b
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* const f = (a, b) => a.mul(b);
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* // df/da = b, df/db = a
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* const g = tf.valueAndGrads(f);
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*
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* const a = tf.tensor1d([2, 3]);
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* const b = tf.tensor1d([-2, -3]);
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* const {value, grads} = g([a, b]);
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*
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* const [da, db] = grads;
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*
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* console.log('value');
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* value.print();
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*
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* console.log('da');
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* da.print();
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* console.log('db');
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* db.print();
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* ```
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*/
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/** @doc {heading: 'Training', subheading: 'Gradients'} */
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function valueAndGrads(f) {
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util.assert(util.isFunction(f), function () { return 'The f passed in valueAndGrads(f) must be a function'; });
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return function (args, dy) {
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util.assert(Array.isArray(args) && args.every(function (arg) { return arg instanceof tensor_1.Tensor; }), function () { return 'The args passed in valueAndGrads(f)(args) must be array of ' +
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'tensors'; });
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util.assert(dy == null || dy instanceof tensor_1.Tensor, function () { return 'The dy passed in valueAndGrads(f)(args, dy) must be a tensor'; });
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var res = engine_1.ENGINE.gradients(function () { return f.apply(void 0, args); }, args, dy);
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if (dy != null) {
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util.assertShapesMatch(res.value.shape, dy.shape, 'The shape of dy passed in valueAndGrads(f)([x1,...], dy) must ' +
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'match the shape returned by f([x1,...])');
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}
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checkGrads(res.grads);
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return res;
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};
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}
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exports.valueAndGrads = valueAndGrads;
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/**
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* Computes and returns the gradient of f(x) with respect to the list of
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* trainable variables provided by `varList`. If no list is provided, it
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* defaults to all trainable variables.
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*
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* ```js
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* const a = tf.variable(tf.tensor1d([3, 4]));
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* const b = tf.variable(tf.tensor1d([5, 6]));
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* const x = tf.tensor1d([1, 2]);
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*
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* // f(a, b) = a * x ^ 2 + b * x
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* const f = () => a.mul(x.square()).add(b.mul(x)).sum();
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* // df/da = x ^ 2, df/db = x
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* const {value, grads} = tf.variableGrads(f);
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*
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* Object.keys(grads).forEach(varName => grads[varName].print());
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* ```
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*
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* @param f The function to execute. f() should return a scalar.
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* @param varList The list of variables to compute the gradients with respect
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* to. Defaults to all trainable variables.
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* @returns An object with the following keys and values:
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* - `value`: The value of the function `f`.
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* - `grads`: A map from the names of the variables to the gradients.
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* If the `varList` argument is provided explicitly and contains a subset of
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* non-trainable variables, this map in the return value will contain keys
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* that map the names of the non-trainable variables to `null`.
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*/
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/** @doc {heading: 'Training', subheading: 'Gradients'} */
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function variableGrads(f, varList) {
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util.assert(util.isFunction(f), function () { return 'The f passed in variableGrads(f) must be a function'; });
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util.assert(varList == null ||
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Array.isArray(varList) && varList.every(function (v) { return v instanceof tensor_1.Variable; }), function () {
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return 'The varList passed in variableGrads(f, varList) must be an array ' +
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'of variables';
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});
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var specifiedVarList = varList != null;
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if (!specifiedVarList) {
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// Get all of the trainable variables.
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varList = [];
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for (var varName in engine_1.ENGINE.registeredVariables) {
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varList.push(engine_1.ENGINE.registeredVariables[varName]);
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}
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}
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var specifiedNonTrainable = specifiedVarList ? varList.filter(function (variable) { return !variable.trainable; }) : null;
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// Prune non-trainable variables.
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var originalVarCount = varList.length;
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varList = varList.filter(function (variable) { return variable.trainable; });
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util.assert(varList.length > 0, function () { return "variableGrads() expects at least one of the input variables to " +
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("be trainable, but none of the " + originalVarCount + " variables is ") +
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"trainable."; });
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var allowNoGradients = true;
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var _a = engine_1.ENGINE.gradients(f, varList, null, allowNoGradients), value = _a.value, grads = _a.grads;
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util.assert(grads.some(function (g) { return g != null; }), function () { return 'Cannot find a connection between any variable and the result of ' +
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'the loss function y=f(x). Please make sure the operations that ' +
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'use variables are inside the function f passed to minimize().'; });
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util.assert(value.rank === 0, function () { return "The f passed in variableGrads(f) must return a scalar, but it " +
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("returned a rank-" + value.rank + " tensor"); });
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var namedGrads = {};
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varList.forEach(function (v, i) {
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if (grads[i] != null) {
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namedGrads[v.name] = grads[i];
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}
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});
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if (specifiedNonTrainable != null) {
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// If varList is explicitly provided and contains non-trainable values,
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// add them to the returned gradients with `null` values.
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specifiedNonTrainable.forEach(function (v) { return namedGrads[v.name] = null; });
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}
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return { value: value, grads: namedGrads };
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}
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exports.variableGrads = variableGrads;
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/**
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* Overrides the gradient computation of a function `f`.
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*
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* Takes a function
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* `f(...inputs, save) => {value: Tensor, gradFunc: (dy, saved) => Tensor[]}`
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* and returns another function `g(...inputs)` which takes the same inputs as
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* `f`. When called, `g` returns `f().value`. In backward mode, custom gradients
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* with respect to each input of `f` are computed using `f().gradFunc`.
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*
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* The `save` function passsed to `f` should be used for saving tensors needed
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* in the gradient. And the `saved` passed to the `gradFunc` is a
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* `NamedTensorMap`, which contains those saved tensor.
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*
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* ```js
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* const customOp = tf.customGrad((x, save) => {
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* // Save x to make sure it's available later for the gradient.
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* save([x]);
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* // Override gradient of our custom x ^ 2 op to be dy * abs(x);
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* return {
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* value: x.square(),
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* // Note `saved.x` which points to the `x` we saved earlier.
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* gradFunc: (dy, saved) => [dy.mul(saved[0].abs())]
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* };
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* });
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*
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* const x = tf.tensor1d([-1, -2, 3]);
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* const dx = tf.grad(x => customOp(x));
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*
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* console.log(`f(x):`);
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* customOp(x).print();
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* console.log(`f'(x):`);
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* dx(x).print();
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* ```
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*
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* @param f The function to evaluate in forward mode, which should return
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* `{value: Tensor, gradFunc: (dy, saved) => Tensor[]}`, where `gradFunc`
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* returns the custom gradients of `f` with respect to its inputs.
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*/
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/** @doc {heading: 'Training', subheading: 'Gradients'} */
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function customGrad(f) {
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return engine_1.ENGINE.customGrad(f);
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}
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exports.customGrad = customGrad;
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function checkGrads(grads) {
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var numNullGradients = grads.filter(function (g) { return g == null; }).length;
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if (numNullGradients > 0) {
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throw new Error("Cannot compute gradient of y=f(x) with respect to x. Make sure that\n the f you passed encloses all operations that lead from x to y.");
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}
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}
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//# sourceMappingURL=gradients.js.map
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